Mid-latitude ionospheric features: Natural complexity in action

2020 ◽  
pp. 25-38
Author(s):  
Philip J. Erickson
Keyword(s):  
Natural Complexity

scholarly journals Ant Queen Egg-Marking Signals: Matching Deceptive Laboratory Simplicity with Natural Complexity

PLoS ONE ◽  
2009 ◽  
Vol 4 (3) ◽  
pp. e4718 ◽  
Author(s):  
Jelle S. van Zweden ◽  
Jürgen Heinze ◽  
Jacobus J. Boomsma ◽  
Patrizia d'Ettorre
Keyword(s):  
Egg Marking ◽  
Natural Complexity

scholarly journals PhytoSFDM version 1.0.0: Phytoplankton Size and Functional Diversity Model

2016 ◽  
Vol 9 (11) ◽  
pp. 4071-4085 ◽  
Author(s):  
Esteban Acevedo-Trejos ◽  
Gunnar Brandt ◽  
S. Lan Smith ◽  
Agostino Merico
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Marine Ecosystem ◽  
Three Dimensional ◽  
Functional Trait ◽  
Global Ocean ◽  
Total Biomass ◽  
Spatially Resolved ◽  
Phytoplankton Communities ◽  
Natural Complexity

Abstract. Biodiversity is one of the key mechanisms that facilitate the adaptive response of planktonic communities to a fluctuating environment. How to allow for such a flexible response in marine ecosystem models is, however, not entirely clear. One particular way is to resolve the natural complexity of phytoplankton communities by explicitly incorporating a large number of species or plankton functional types. Alternatively, models of aggregate community properties focus on macroecological quantities such as total biomass, mean trait, and trait variance (or functional trait diversity), thus reducing the observed natural complexity to a few mathematical expressions. We developed the PhytoSFDM modelling tool, which can resolve species discretely and can capture aggregate community properties. The tool also provides a set of methods for treating diversity under realistic oceanographic settings. This model is coded in Python and is distributed as open-source software. PhytoSFDM is implemented in a zero-dimensional physical scheme and can be applied to any location of the global ocean. We show that aggregate community models reduce computational complexity while preserving relevant macroecological features of phytoplankton communities. Compared to species-explicit models, aggregate models are more manageable in terms of number of equations and have faster computational times. Further developments of this tool should address the caveats associated with the assumptions of aggregate community models and about implementations into spatially resolved physical settings (one-dimensional and three-dimensional). With PhytoSFDM we embrace the idea of promoting open-source software and encourage scientists to build on this modelling tool to further improve our understanding of the role that biodiversity plays in shaping marine ecosystems.

scholarly journals From Natural Complexity to Biomimetic Simplification: The Realization of Bionic Fish Inspired by the Cownose Ray

2019 ◽  
Vol 26 (3) ◽  
pp. 27-38
Author(s):  
Yueri Cai ◽  
Shusheng Bi ◽  
Guoyuan Li ◽  
Hans Petter Hildre ◽  
Houxiang Zhang
Keyword(s):  
Cownose Ray ◽  
Natural Complexity

Introduction: What Is Complexity?

2017 ◽  
Author(s):  
Paul Charbonneau
Keyword(s):  
Complex Systems ◽  
Computational Model ◽  
Computer Programs ◽  
Dissipative Systems ◽  
Ant Colonies ◽  
Natural Complex ◽  
Hands On ◽  
Complex Phenomena ◽  
Natural Complexity

This book investigates complex systems that are idealizations of naturally occurring phenomena characterized by the autonomous generation of structures and patterns at macroscopic scales. It provides material and guidance to allow the reader to learn about complexity through hands-on experimentation with complex systems with the aid of computer programs. Each chapter thus presents a simple computational model of natural complex phenomena ranging from avalanches and earthquakes to solar flares, epidemics, and ant colonies. This introductory chapter explains what complexity is, with emphasis on the fact that defining it is not a simple endeavor, and that it is not the same as randomness or chaos. It also shows that open dissipative systems are complex and clarifies what natural complexity means. Finally, it describes the computer programs listed in this book and suggests materials for further reading about complexity.

Exploiting Natural Complexity: Synthetic Terpenoid‐Alkaloids by Regioselective and Diastereoselective Hydroaminoalkylation Catalysis

ChemCatChem ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 3871-3876 ◽  
Author(s):  
Rebecca C. DiPucchio ◽  
Sorin‐Claudiu Rosca ◽  
Gayathri Athavan ◽  
Laurel L. Schafer
Keyword(s):  
Natural Complexity

scholarly journals PhytoSFDM version 1.0.0: Phytoplankton Size and Functional Diversity Model

2016 ◽  
Author(s):  
Esteban Acevedo-Trejos ◽  
Gunnar Brandt ◽  
S. Lan Smith ◽  
Agostino Merico
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Marine Ecosystem ◽  
Functional Trait ◽  
Total Biomass ◽  
Spatially Resolved ◽  
Flexible Response ◽  
Phytoplankton Communities ◽  
Marine Ecosystem Models ◽  
Natural Complexity

Abstract. Biodiversity is one of the key mechanisms that facilitate the adaptive response of planktonic communities to a fluctuating environment. How to allow for such a flexible response in marine ecosystem models is, however, not entirely clear. One particular way is to resolve the natural complexity of phytoplankton communities by explicitly incorporating a large number of species or plankton functional types. Alternatively, models of aggregate community properties focus on macroecological quantities such as total biomass, mean trait, and trait variance (or functional trait diversity), thus reducing the observed natural complexity to a few mathematical expressions. We developed the modelling tool PhytoSFDM, which can resolve species discretely and can capture aggregate community properties. The tool also provides a set of methods for treating diversity under realistic oceanographic settings. This model is coded in Python and is distributed as an open-source software. PhytoSFDM is implemented in a 0D physical scheme and can be applied to any location of the world oceans. We show that aggregate-community models reduce computational complexity while preserving relevant macroecological features of phytoplankton communities. Compared to species-explicit models, aggregate models are more manageable in terms of number of equations and have faster computational times. Further developments of this tool should address the caveats associated with the assumptions of aggregate community models and on implementations into spatially resolved physical settings (1D and 3D). With PhytoSFDM we embrace the idea of promoting open source software and encourage scientists to build on this modelling tool to further improve our understanding of the role that biodiversity plays in shaping marine ecosystems.

Natural Complexity

2017 ◽  
Author(s):  
Paul Charbonneau
Keyword(s):  
Graduate Students ◽  
Forest Fires ◽  
Mathematical Knowledge ◽  
Computational Models ◽  
Complex Structures ◽  
Natural Phenomena ◽  
Source Codes ◽  
Hands On ◽  
Epidemic Diseases ◽  
Natural Complexity

This book provides a short, hands-on introduction to the science of complexity using simple computational models of natural complex systems—with models and exercises drawn from physics, chemistry, geology, and biology. By working through the models and engaging in additional computational explorations suggested at the end of each chapter, readers very quickly develop an understanding of how complex structures and behaviors can emerge in natural phenomena as diverse as avalanches, forest fires, earthquakes, chemical reactions, animal flocks, and epidemic diseases. This book provides the necessary topical background, complete source codes in Python, and detailed explanations for all computational models. Ideal for undergraduates, beginning graduate students, and researchers in the physical and natural sciences, this unique handbook requires no advanced mathematical knowledge or programming skills and is suitable for self-learners with a working knowledge of precalculus and high-school physics. The book enables readers to identify and quantify common underlying structural and dynamical patterns shared by the various systems and phenomena it examines, so that they can form their own answers to the questions of what natural complexity is and how it arises.

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